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Exploration and casting of large scale microscopic pathways for shale using electrodeposition

Author

Listed:
  • Jin, Xu
  • Wang, Xiaoqi
  • Yan, Weipeng
  • Meng, Siwei
  • Liu, Xiaodan
  • Jiao, Hang
  • Su, Ling
  • Zhu, Rukai
  • Liu, He
  • Li, Jianming

Abstract

In unconventional petroleum reservoirs, such as shale gas, shale oil, tight oil, and tight gas reservoirs, the microscopic pore structure, namely, the size, geometry, distribution, and interconnected relations of the pores and throats of a shale rock, directly affects the porosity, storage, and permeability. Studies related to the microscopic pore structure of shale are considered important for evaluating shale resources and for elucidating their distribution characteristics; additionally, these studies aim to improve the productivity and recovery ratio of both oil and gas. Therefore, methods that can accurately characterize the microscopic pore structure of shale have received considerable attention. In this study, we used the electrodeposition method to fill the interconnected pores of a rock sheet with metal and then used selective dissolution of the rock portion of the rock sheet to obtain the metal complex of the pore-throat structure. The structure and morphology of the obtained metal complex, which represents the microscopic pore structure of the shale, are characterized by a scanning electron microscope (SEM). By combining electrochemical deposition and SEM images, we could directly observe the three-dimensional microstructure of the shale at a scale smaller than 50 nm with a large observation area. Additionally, the surface areas of the connected pores and throats of the shale were calculated.

Suggested Citation

  • Jin, Xu & Wang, Xiaoqi & Yan, Weipeng & Meng, Siwei & Liu, Xiaodan & Jiao, Hang & Su, Ling & Zhu, Rukai & Liu, He & Li, Jianming, 2019. "Exploration and casting of large scale microscopic pathways for shale using electrodeposition," Applied Energy, Elsevier, vol. 247(C), pages 32-39.
  • Handle: RePEc:eee:appene:v:247:y:2019:i:c:p:32-39
    DOI: 10.1016/j.apenergy.2019.03.197
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    References listed on IDEAS

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    1. Weijermars, Ruud, 2015. "Shale gas technology innovation rate impact on economic Base Case – Scenario model benchmarks," Applied Energy, Elsevier, vol. 139(C), pages 398-407.
    2. Wang, Ke & Li, Haitao & Wang, Junchao & Jiang, Beibei & Bu, Chengzhong & Zhang, Qing & Luo, Wei, 2017. "Predicting production and estimated ultimate recoveries for shale gas wells: A new methodology approach," Applied Energy, Elsevier, vol. 206(C), pages 1416-1431.
    3. Yuan, Jiehui & Luo, Dongkun & Feng, Lianyong, 2015. "A review of the technical and economic evaluation techniques for shale gas development," Applied Energy, Elsevier, vol. 148(C), pages 49-65.
    4. Zou, Youqin & Yang, Changbing & Wu, Daishe & Yan, Chun & Zeng, Masun & Lan, Yingying & Dai, Zhenxue, 2016. "Probabilistic assessment of shale gas production and water demand at Xiuwu Basin in China," Applied Energy, Elsevier, vol. 180(C), pages 185-195.
    5. Middleton, Richard S. & Gupta, Rajan & Hyman, Jeffrey D. & Viswanathan, Hari S., 2017. "The shale gas revolution: Barriers, sustainability, and emerging opportunities," Applied Energy, Elsevier, vol. 199(C), pages 88-95.
    6. Niu, Mengting & Wang, Sha & Han, Xiangxin & Jiang, Xiumin, 2013. "Yield and characteristics of shale oil from the retorting of oil shale and fine oil-shale ash mixtures," Applied Energy, Elsevier, vol. 111(C), pages 234-239.
    7. Saif, Tarik & Lin, Qingyang & Butcher, Alan R. & Bijeljic, Branko & Blunt, Martin J., 2017. "Multi-scale multi-dimensional microstructure imaging of oil shale pyrolysis using X-ray micro-tomography, automated ultra-high resolution SEM, MAPS Mineralogy and FIB-SEM," Applied Energy, Elsevier, vol. 202(C), pages 628-647.
    8. Han, X.X. & Jiang, X.M. & Cui, Z.G., 2009. "Studies of the effect of retorting factors on the yield of shale oil for a new comprehensive utilization technology of oil shale," Applied Energy, Elsevier, vol. 86(11), pages 2381-2385, November.
    9. Kim, Tae Hong & Cho, Jinhyung & Lee, Kun Sang, 2017. "Evaluation of CO2 injection in shale gas reservoirs with multi-component transport and geomechanical effects," Applied Energy, Elsevier, vol. 190(C), pages 1195-1206.
    10. Chen, Yuntian & Jiang, Su & Zhang, Dongxiao & Liu, Chaoyang, 2017. "An adsorbed gas estimation model for shale gas reservoirs via statistical learning," Applied Energy, Elsevier, vol. 197(C), pages 327-341.
    11. Weijermars, Ruud, 2014. "US shale gas production outlook based on well roll-out rate scenarios," Applied Energy, Elsevier, vol. 124(C), pages 283-297.
    12. Nguyen, Phong & Carey, J. William & Viswanathan, Hari S. & Porter, Mark, 2018. "Effectiveness of supercritical-CO2 and N2 huff-and-puff methods of enhanced oil recovery in shale fracture networks using microfluidic experiments," Applied Energy, Elsevier, vol. 230(C), pages 160-174.
    13. Saif, Tarik & Lin, Qingyang & Gao, Ying & Al-Khulaifi, Yousef & Marone, Federica & Hollis, David & Blunt, Martin J. & Bijeljic, Branko, 2019. "4D in situ synchrotron X-ray tomographic microscopy and laser-based heating study of oil shale pyrolysis," Applied Energy, Elsevier, vol. 235(C), pages 1468-1475.
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